Browsing by Author "Sakamoto, Naohisa"

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    Lessons Learned from Large Data Visualization Software Development for the K computer
    (The Eurographics Association, 2020) Nonaka, Jorji; Sakamoto, Naohisa; Gillmann, Christina and Krone, Michael and Reina, Guido and Wischgoll, Thomas
    High Performance Computing (HPC) always had a close relationship with visualization as we can remember the landmark report on ''Visualization in Scientific Computing'', which was credited to have coined the term Scientific Visualization (SciVis). K computer, a Japanese flagship HPC system, appeared in 2011 as the most powerful supercomputer in the Top500 list, and as other similar HPC systems in that ranking, it was designed to enable ''Grand Challenge'' scientific computing with unprecedented scale and size. RIKEN Center for Computational Science (RIKEN R-CCS) operated and provided the K computer's computational resources to the HPC community for almost 8 years until it was decommissioned in 2019. Considering that most of the scientific computing results were publicly presented in the form of visual images and movies, we can infer that the SciVis was widely applied for assisting the domain scientists with their end-to-end scientific computing workflows. In addition to the traditional visualization applications, various others large data visualization software development were conducted in order to tackle the increased size and amount of the simulation outputs. RIKEN R-CCS participated in some of these development and deployment dealing with several environmental and human factors. Although we have no precise statistics regarding the visualization software usage, in this paper, we would like to present some findings and lessons learned from the large data visualization software development in the K computer environment.
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    Reflections on the Developments of Visual Analytics Systems for the K Computer System Log Data
    (The Eurographics Association, 2023) Nonaka, Jorji; Fujita, Keijiro; Fujiwara, Takanori; Sakamoto, Naohisa; Yamamoto, Keiji; Terai, Masaaki; Tsukamoto, Toshiyuki; Shoji, Fumiyoshi; Gillmann, Christina; Krone, Michael; Reina, Guido; Wischgoll, Thomas
    Flagship-class high-performance computing (HPC) systems, also known as supercomputers, are large, complex systems that require particular attention for continuous and long-term stable operations. The K computer was a Japanese flagship-class supercomputer ranked as the fastest supercomputer in the Top500 ranking when it first appeared. It was composed of more than eighty thousand compute nodes and consumed more than 12 MW when running the LINPACK benchmark for the Top500 submission. A combined power substation, with a natural gas co-generation system (CGS), was used for the power supply, and also a large air/water cooling facility was used to extract the massive heat generated from this HPC system. During the years of its regular operation, a large log dataset has been generated from the K computer system and its facility, and several visual analytics systems have been developed to better understand the K computer's behavior during the operation as well as the probable correlation of operational temperature with the critical hardware failures. In this paper, we will reflect on these visual analytics systems, mainly developed by graduate students, intended to be used by different types of end users on the HPC site. In addition, we will discuss the importance of collaborative development involving the end users, and also the importance of technical people in the middle for assisting in the deployment and possible continuation of the developed systems.